{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "from torch.autograd import Variable\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from functools import reduce\n",
    "import operator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Tensors are similar to numpy's ndarrays, with the addition being that Tensors can also be used on a GPU to accelerate computing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "-6.0309e-39  4.5587e-41 -6.0309e-39\n",
       " 4.5587e-41         nan  9.8091e-45\n",
       " 1.1451e+01  4.6776e-05  1.9320e+02\n",
       " 6.6388e-07  4.4814e+01  3.1393e+03\n",
       " 7.7708e+02  1.7993e-07 -6.0307e-39\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.Tensor(5, 3); x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.6106  0.7070  0.0239\n",
       " 0.9383  0.3874  0.4347\n",
       " 0.8379  0.2915  0.5832\n",
       " 0.8180  0.7388  0.0405\n",
       " 0.2994  0.6206  0.2632\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.rand(5, 3); x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 3])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y = torch.rand(5, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1.6099  1.3226  0.4392\n",
       " 1.3254  1.3003  0.6672\n",
       " 1.2006  0.9704  0.6622\n",
       " 1.7680  1.2659  0.9139\n",
       " 0.5735  1.5582  0.7811\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1.6099  1.3226  0.4392\n",
       " 1.3254  1.3003  0.6672\n",
       " 1.2006  0.9704  0.6622\n",
       " 1.7680  1.2659  0.9139\n",
       " 0.5735  1.5582  0.7811\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.add(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1.6099  1.3226  0.4392\n",
       " 1.3254  1.3003  0.6672\n",
       " 1.2006  0.9704  0.6622\n",
       " 1.7680  1.2659  0.9139\n",
       " 0.5735  1.5582  0.7811\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = torch.Tensor(5, 3)\n",
    "torch.add(x, y, out=result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1.6099  1.3226  0.4392\n",
       " 1.3254  1.3003  0.6672\n",
       " 1.2006  0.9704  0.6622\n",
       " 1.7680  1.2659  0.9139\n",
       " 0.5735  1.5582  0.7811\n",
       "[torch.FloatTensor of size 5x3]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# anything ending in '_' is an in-place operation\n",
    "y.add_(x) # adds x to y in-place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.7070\n",
       " 0.3874\n",
       " 0.2915\n",
       " 0.7388\n",
       " 0.6206\n",
       "[torch.FloatTensor of size 5]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standard numpy-like indexing with all bells and whistles\n",
    "x[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Numpy Bridge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The torch Tensor and numpy array will share their underlying memory locations, and changing one will change the other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Converting torch Tensor to numpy Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1\n",
       " 1\n",
       " 1\n",
       " 1\n",
       " 1\n",
       "[torch.FloatTensor of size 5]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = torch.ones(5)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  1.,  1.], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a.numpy()\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 2\n",
      " 2\n",
      " 2\n",
      " 2\n",
      " 2\n",
      "[torch.FloatTensor of size 5]\n",
      "\n",
      "[ 2.  2.  2.  2.  2.]\n"
     ]
    }
   ],
   "source": [
    "a.add_(1)\n",
    "print(a)\n",
    "print(b) # see how the numpy array changed in value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Converting numpy Array to torch Tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.  2.  2.  2.  2.]\n",
      "\n",
      " 2\n",
      " 2\n",
      " 2\n",
      " 2\n",
      " 2\n",
      "[torch.DoubleTensor of size 5]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = np.ones(5)\n",
    "b = torch.from_numpy(a)\n",
    "np.add(a, 1, out=a)\n",
    "print(a)\n",
    "print(b) # see how changing the np array changed the torch Tensor automatically"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### CUDA Tensors\n",
    "\n",
    "Tensors can be moved onto GPU using the `.cuda` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 2.2205  2.0297  0.4631\n",
       " 2.2637  1.6877  1.1020\n",
       " 2.0385  1.2619  1.2454\n",
       " 2.5860  2.0047  0.9544\n",
       " 0.8729  2.1788  1.0442\n",
       "[torch.cuda.FloatTensor of size 5x3 (GPU 0)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x.cuda()\n",
    "y = y.cuda()\n",
    "\n",
    "x+y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Autograd: automatic differentiation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Central to all neural networks in PyTorch is the `autograd` package.\n",
    "\n",
    "The `autograd` package provides automatic differentiation for all operations on Tensors.  \n",
    "It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different. \n",
    "\n",
    "`autograd.Variable` is the central class of the package. \n",
    "It wraps a Tensor, and supports nearly all of operations defined on it. Once you finish your computation you can call `.backward()` and have all the gradients computed automatically.\n",
    "\n",
    "You can access the raw tensor through the `.data` attribute, while the gradient w.r.t. this variable is accumulated into `.grad`.\n",
    "\n",
    "If you want to compute the derivatives, you can call `.backward()` on a `Variable`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 1  1\n",
       " 1  1\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = Variable(torch.ones(2, 2), requires_grad = True); x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 3  3\n",
       " 3  3\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x + 2; y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.autograd._functions.basic_ops.AddConstant at 0x7f142c439888>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.creator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 27  27\n",
       " 27  27\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = y * y * 3; z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 27\n",
       "[torch.FloatTensor of size 1]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = z.mean(); out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<torch.autograd._functions.basic_ops.MulConstant object at 0x7f142c439348>\n",
      "<torch.autograd._functions.basic_ops.Mul object at 0x7f142c4394c8>\n"
     ]
    }
   ],
   "source": [
    "# You never have to look at these in practice - this is just showing how the\n",
    "#   computation graph is stored\n",
    "print(out.creator.previous_functions[0][0])\n",
    "print(out.creator.previous_functions[0][0].previous_functions[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "out.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 4.5000  4.5000\n",
       " 4.5000  4.5000\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# d(out)/dx\n",
    "x.grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "You should have got a matrix of `4.5`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Because PyTorch is a dynamic computation framework, we can take the gradients of all kinds of interesting computations, even loops!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "x = torch.randn(3)\n",
    "x = Variable(x, requires_grad = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y = x * 2\n",
    "while y.data.norm() < 1000:\n",
    "    y = y * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 1479.7150\n",
       "  466.4501\n",
       "   24.2099\n",
       "[torch.FloatTensor of size 3]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "gradients = torch.FloatTensor([0.1, 1.0, 0.0001])\n",
    "y.backward(gradients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "  51.2000\n",
       " 512.0000\n",
       "   0.0512\n",
       "[torch.FloatTensor of size 3]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Neural networks can be constructed using the `torch.nn` package.\n",
    "\n",
    "An `nn.Module` contains layers, and a method `forward(input)`that returns the `output`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 6, 5) # 1 input channel, 6 output channels, 5x5 kernel\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.fc1   = nn.Linear(16*5*5, 120) # like keras' Dense()\n",
    "        self.fc2   = nn.Linear(120, 84)\n",
    "        self.fc3   = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
    "        x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n",
    "        x = x.view(-1, self.num_flat_features(x))\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "    \n",
    "    def num_flat_features(self, x):\n",
    "        return reduce(operator.mul, x.size()[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net (\n",
       "  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (fc1): Linear (400 -> 120)\n",
       "  (fc2): Linear (120 -> 84)\n",
       "  (fc3): Linear (84 -> 10)\n",
       ")"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Net(); net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "You just have to define the `forward` function, and the `backward` function (where gradients are computed) is automatically defined for you using `autograd`.\n",
    "\n",
    "The learnable parameters of a model are returned by `net.parameters()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "net.cuda();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, torch.Size([6, 1, 5, 5]))"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = list(net.parameters())\n",
    "len(params), params[0].size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The input to the forward is a `Variable`, and so is the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 0.0182  0.2793 -0.0525  0.2080 -0.0320 -0.0230  0.1369  0.0551  0.0196  0.0240\n",
       "[torch.cuda.FloatTensor of size 1x10 (GPU 0)]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input = Variable(torch.randn(1, 1, 32, 32)).cuda()\n",
    "out = net(input); out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "net.zero_grad() # zeroes the gradient buffers of all parameters\n",
    "out.backward(torch.randn(1, 10).cuda()) # backprops with random gradients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "source": [
    "A loss function takes the (output, target) pair of inputs, and computes a value that estimates how far away the output is from the target. There are [several different loss functions under the nn package](http://pytorch.org/docs/nn.html#loss-functions). A simple loss is: `nn.MSELoss` which computes the mean-squared error between the input and the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 37.9610\n",
       "[torch.cuda.FloatTensor of size 1 (GPU 0)]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = net(input)\n",
    "target = Variable(torch.range(1, 10)).cuda()  # a dummy target, for example\n",
    "loss = nn.MSELoss()(output, target); loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now, if you follow `loss` in the backward direction, using it's `.creator` attribute, you will see a graph of computations that looks like this:\n",
    "\n",
    "```\n",
    "input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d  \n",
    "      -> view -> linear -> relu -> linear -> relu -> linear \n",
    "      -> MSELoss\n",
    "      -> loss\n",
    "```\n",
    "\n",
    "So, when we call `loss.backward()`, the whole graph is differentiated w.r.t. the loss, and all Variables in the graph will have their `.grad` Variable accumulated with the gradient.\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv1.bias.grad before backward\n",
      "Variable containing:\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      " 0\n",
      "[torch.cuda.FloatTensor of size 6 (GPU 0)]\n",
      "\n",
      "conv1.bias.grad after backward\n",
      "Variable containing:\n",
      "1.00000e-02 *\n",
      " -2.1977\n",
      " -5.5820\n",
      "  1.5586\n",
      " -7.5659\n",
      " -3.8961\n",
      " -1.8429\n",
      "[torch.cuda.FloatTensor of size 6 (GPU 0)]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# now we shall call loss.backward(), and have a look at gradients before and after\n",
    "net.zero_grad() # zeroes the gradient buffers of all parameters\n",
    "print('conv1.bias.grad before backward')\n",
    "print(net.conv1.bias.grad)\n",
    "loss.backward()\n",
    "print('conv1.bias.grad after backward')\n",
    "print(net.conv1.bias.grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "optimizer = optim.SGD(net.parameters(), lr = 0.01)\n",
    "\n",
    "# in your training loop:\n",
    "optimizer.zero_grad() # zero the gradient buffers\n",
    "output = net(input)\n",
    "loss = nn.MSELoss()(output, target)\n",
    "loss.backward()\n",
    "optimizer.step() # Does the update"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Example complete process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For vision, there is a package called `torch.vision`, that \n",
    "has data loaders for common datasets such as Imagenet, CIFAR10, MNIST, etc. and data transformers for images.\n",
    "\n",
    "For this tutorial, we will use the CIFAR10 dataset. \n",
    "\n",
    "### Training an image classifier\n",
    "\n",
    "We will do the following steps in order:\n",
    "\n",
    "1. Load and normalizing the CIFAR10 training and test datasets using `torchvision`\n",
    "1. Define a Convolution Neural Network\n",
    "1. Define a loss function\n",
    "1. Train the network on the training data\n",
    "1. Test the network on the test data\n",
    "\n",
    "### 1. Loading and normalizing CIFAR10\n",
    "\n",
    "Using `torch.vision`, it's extremely easy to load CIFAR10. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import torchvision\n",
    "from torchvision import transforms, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# The output of torchvision datasets are PILImage images of range [0, 1].\n",
    "# We transform them to Tensors of normalized range [-1, 1]\n",
    "transform=transforms.Compose([transforms.ToTensor(),\n",
    "                              transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "                             ])\n",
    "trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, \n",
    "                                          shuffle=True, num_workers=2)\n",
    "\n",
    "testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=32, \n",
    "                                          shuffle=False, num_workers=2)\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def imshow(img):\n",
    "    plt.imshow(np.transpose((img / 2 + 0.5).numpy(), (1,2,0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plane truck   cat  frog\n"
     ]
    },
    {
     "data": {
      "image/png": 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EKE1Ukx5J9puaEs67tRbpvfQwSAjCgMpqstTdbytLs95kNj2gzCua9aYfK0leFEynY7LM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KENcGYFLC912fS8uj4UhgFllZLnCXlxFs3SE4JX2222jJB53PXJQgc7B9/gwLPBIbmSZNkUcWIw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\nxxzsjRifLOj1HBKkMgfMJnOUqWm1Qlo9z+CenXByNKTXWccs9VDKmrt37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OXpbbrtNkrA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\nXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14203c00f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# print images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(' '.join('%5s'%classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 2. Define a Convolution Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
    "        self.pool  = nn.MaxPool2d(2,2)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.fc1   = nn.Linear(16*5*5, 120)\n",
    "        self.fc2   = nn.Linear(120, 84)\n",
    "        self.fc3   = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 16*5*5)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "net = Net().cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 2. Define a Loss function and optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss().cuda()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 3. Train the network\n",
    "\n",
    "This is when things start to get interesting.\n",
    "\n",
    "We simply have to loop over our data iterator, and feed the inputs to \n",
    "the network and optimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for epoch in range(2): # loop over the dataset multiple times\n",
    "    \n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs\n",
    "        inputs, labels = data\n",
    "        # wrap them in Variable\n",
    "        inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())\n",
    "        \n",
    "        # forward + backward + optimize\n",
    "        optimizer.zero_grad()\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()        \n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.data[0]\n",
    "        if i % 2000 == 1999: # print every 2000 mini-batches\n",
    "            print('[%d, %5d] loss: %.3f' % (epoch+1, i+1, running_loss / 2000))\n",
    "            running_loss = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We will check what the model has learned by predicting the class label, and checking it against the ground-truth. If the prediction is correct, we add the sample to the list of correct predictions.\n",
    "\n",
    "First, let's display an image from the test set to get familiar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'  cat  ship  ship plane'"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kshwb9NOOiNZz5+bJ817XJXVdEoYCY93vH40zpsURUDEa7wEuopOBJW5olHDnrMglyoYo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\nBwFeQ0XY7fSPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14201ff438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# print images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "' '.join('%5s'%classes[labels[j]] for j in range(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Okay, now let us see what the neural network thinks these examples above are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'  cat  ship   car plane'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs = net(Variable(images).cuda())\n",
    "_, predicted = torch.max(outputs.data, 1)\n",
    "' '.join('%5s'% classes[predicted[j][0]] for j in range(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The results seem pretty good. Let us look at how the network performs on the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 38 %\n"
     ]
    }
   ],
   "source": [
    "correct,total = 0,0\n",
    "for data in testloader:\n",
    "    images, labels = data\n",
    "    outputs = net(Variable(images).cuda())\n",
    "    _, predicted = torch.max(outputs.data, 1)\n",
    "    total += labels.size(0)\n",
    "    correct += (predicted == labels.cuda()).sum()\n",
    "\n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "That looks way better than chance, which is 10% accuracy (randomly picking a class out of 10 classes)."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
